System and method for using a mobile electronic device to optimize an energy management system

ABSTRACT

Embodiments of the invention comprise systems and methods for using the geographic location of networked consumer electronics devices as indications of occupancy of a structure for purposes of automatically adjusting the temperature setpoint on a thermostatic HVAC control. At least one thermostat is located inside a structure and is used to control an HVAC system in the structure. At least one mobile electronic device is used to indicate the state of occupancy of the structure. The state of occupancy is used to alter the setpoint on the thermostatic HVAC control to reduce unneeded conditioning of unoccupied spaces.

CROSS-REFERENCE TO RELATED APPLICATIONS

Any and all applications for which a foreign or domestic priority claimis identified in the Application Data Sheet, or any correction thereto,are hereby incorporated by reference under 37 CFR 1.57.

BACKGROUND OF THE INVENTION Field of the Invention

This invention relates to the use of thermostatic HVAC and other energymanagement controls that are connected to a computer network. Morespecifically, the present invention pertains to the use of geographicmovement of network-connected mobile devices to occupancy to inform anenergy management system, to provide enhanced efficiency, and to verifydemand response.

Description of the Related Art

Heating and cooling systems for buildings (heating, ventilation andcooling, or HVAC systems) have been controlled for decades bythermostats. At the most basic level, a thermostat includes a means toallow a user to set a desired temperature, a means to sense actualtemperature, and a means to signal the heating and/or cooling devices toturn on or off in order to try to change the actual temperature to equalthe desired temperature. The most basic versions of thermostats usecomponents such as a coiled bi-metallic spring to measure actualtemperature and a mercury switch that opens or completes a circuit whenthe spring coils or uncoils with temperature changes. More recently,electronic digital thermostats have become prevalent. These thermostatsuse solid-state devices such as thermistors or thermal diodes to measuretemperature, and microprocessor-based circuitry to control the switchand to store and operate based upon user-determined protocols fortemperature vs. time.

These programmable thermostats generally offer a very restrictive userinterface, limited by the cost of the devices, the limited real estateof the small wall-mounted boxes, and the inability to take into accountmore than two variables: the desired temperature set by the user, andthe ambient temperature sensed by the thermostat. Users can generallyonly set one series of commands per day, and in order to change oneparameter (e.g., to change the late-night temperature) the user oftenhas to cycle through several other parameters by repeatedly pressing oneor two buttons.

Because the interface of programmable thermostats is so poor, thesignificant theoretical savings that are possible with them (sometimescited as 25% of heating and cooling costs) are rarely realized. Inpractice, studies have found that more than 50% of users never programtheir thermostats at all. Significant percentages of the thermostatsthat are programmed are programmed sub-optimally, in part because, onceprogrammed, people tend to not to re-invest the time needed to changethe settings very often.

A second problem with standard programmable thermostats is that theyrepresent only a small evolutionary step beyond the first, purelymechanical thermostats. Like the first thermostats, they only have twoinput signals—ambient temperature and the preset desired temperature.The entire advance with programmable thermostats is that they can shiftbetween multiple present temperatures at different times withoutreal-time involvement of a human being.

Because most thermostats control HVAC systems that do not offerinfinitely variable output, traditional thermostats are designed topermit the temperature as seen by the thermostat to vary above and belowthe setpoint to prevent the HVAC system from constantly and rapidlycycling on and off, which is inefficient and harmful to the HVAC system.The temperature range in which the thermostat allows the controlledenvironment to drift is known as both the dead zone and, more formally,the hysteresis zone. The hysteresis zone is frequently set at +/−1degree Fahrenheit. Thus if the setpoint is 68 degrees, in the heatingcontext the thermostat will allow the inside temperature to fall to 67degrees before turning the heating system on, and will allow it to riseto 69 degrees before turning it off again.

As energy prices rise, more attention is being paid to ways of reducingenergy consumption. Because energy consumption is directly proportionalto setpoint—that is, the further a given setpoint diverges from thebalance point (the inside temperature assuming no HVAC activity) in agiven house under given conditions, the higher energy consumption willbe to maintain temperature at that setpoint), energy will be saved byvirtually any strategy that over a given time frame lowers the averageheating setpoint or raises the average cooling setpoint. Conventionalprogrammable thermostats allow homeowners to save money and energy bypre-programming setpoint changes based upon comfort or schedule. Forexample, in the summer, allowing the setpoint to rise by several degrees(or even shutting off the air conditioner) when the home is unoccupiedwill generally save significantly on energy. But such thermostats haveproven to be only minimally effective in practice. Because they havesuch primitive user interfaces, they are difficult to program, and somany users never bother at all, or set them up once and do not alter theprogramming even if their schedules change.

In the hotel industry, the heating and cooling decisions made in hundredor even thousands of individual rooms with independently controlled HVACsystems are aggregated into a single energy bill, so hotel owners andmanagers are sensitive to energy consumption by those systems. Hotelguests often turn the air conditioner to a low temperature setting andthen leave the room for hours at a time, thereby wasting considerableenergy. An approach commonly used outside of the United States to combatthis problem is to use a keycard to control the HVAC system, such thatguests place the keycard into a slot mounted on the wall near the doorof the room which then triggers the lights and HVAC system to power up,and turn them off when the guest removes the card upon leaving the room.However, because most hotels give each guest two cards, it is easy tosimply leave the extra card in the slot, thus defeating the purpose ofthe system. Recently, systems have been introduced in which a motionsensor is connected to the control circuitry for the HVAC system. If nomotion is detected in the room for some predetermined interval, thesystem concludes that the room is unoccupied, and turns off the HVACsystem or alters the setpoint to a more economical level. When themotion sensor detects motion (which is assumed to coincide with thereturn of the guest), the HVAC system resets to the guest's chosensetting.

Adding occupancy detection capability to residential HVAC systems couldalso add considerable value in the form of energy savings withoutsignificant tradeoff in terms of comfort. But the systems used in hotelsdo not easily transfer to the single-family residential context. Hotelrooms tend to be small enough that a single motion sensor is sufficientto determine with a high degree of accuracy whether or not the room isoccupied. A single motion sensor in the average home today would havelimited value because there are likely to be many places one or morepeople could be home and active yet invisible to the motion sensor. Themost economical way to include a motion sensor in a traditionalprogrammable thermostat would be to build it into the thermostat itself.But thermostats are generally located in hallways, and thus are unlikelyto be exposed to the areas where people tend to spend their time. Wiringa home with multiple motion sensors in order to maximize the chances ofdetecting occupants would involve considerable expense, both for thesensors themselves and for the considerable cost of installation,especially in the retrofit market. Yet if control is ceded to asingle-sensor system that cannot reliably detect presence, the resultingerrors would likely lead the homeowner to reject the system.

Although progress in residential HVAC control has been slow, tremendoustechnological change has come to the tools used for personalcommunication. When programmable thermostats were first offered,telephones were virtually all tethered by wires to a wall jack. But nowa large percentage of the population carries at least one mobile devicecapable of sending and receiving voice or data or even video (or acombination thereof) from almost anywhere by means of a wirelessnetwork. These devices create the possibility that a consumer can, withan appropriate mobile device and a network-enabled HVAC system, controlhis or her HVAC system even when away from home. But systems that relayon active management decisions by consumers are likely to yieldsub-optimal energy management outcomes, because consumers are unlikelyto devote the attention and effort required to fully optimize energy useon a daily basis.

Many new mobile devices now incorporate another significant newtechnology—the ability to geolocate the device (and thus, presumably,the user of the device). One method of locating such devices uses theGlobal Positioning System (GPS). The GPS system uses a constellation oforbiting satellites with very precise clocks to triangulate the positionof a device anywhere on earth based upon arrival times of signalsreceived from those satellites by the device. Another approach togeolocation triangulates using signals from multiple cell phone towers.Such systems can enable a variety of so-called “location based services”to users of enabled devices. These services are generally thought of asaids to commerce like pointing users to restaurants or gas stations,etc.

It would be desirable to provide a system that could detect the locationof regular occupants of a home or other structure without requiring theinstallation of additional hardware; that could accurately use suchgeospatial information in order to detect and predict occupancy, andcould optimize energy consumption based upon dynamic and individuallyconfigurable heuristics.

SUMMARY OF THE INVENTION

In one embodiment, the invention comprises a thermostat attached to anHVAC system, a local network connecting the thermostat to a largernetwork such as the Internet, and one or more geolocation-enabled mobiledevices attached to the network, and a server in bi-directionalcommunication with a plurality of such thermostats and devices. Theserver pairs each thermostat with one or more geolocation-enabled mobiledevices which are determined to be associated with the occupants of homein which the thermostat is located.

The server logs the ambient temperature sensed by each thermostat vs.time and the signals sent by the thermostats to their HVAC systems. Theserver also monitors and logs the geolocation data related to thegeolocation-enabled mobile devices associated with each thermostat.Based on the locations and movement evidenced by geolocation data, theserver instructs the thermostat to change temperature settings betweenthose optimized for occupied and unoccupied states at the appropriatetimes based on the evolving geolocation data.

One embodiment of the invention is directed to a method for varyingtemperature setpoints for an HVAC system. The method comprisesdetermining the geographic location of a mobile electronic device thatis connected to a network and associating said mobile electronic deviceto a structure having a known geographic location that contains one ormore networked climate control devices. In addition, the methodcomprises determining whether the location of said mobile electronicdevice indicates occupancy of said structure by a person associated withsaid mobile electronic device and adjusting the temperature setpoint onsaid controller for an HVAC system for said structure based upon whetheror not said structure is deemed to be occupied.

In another embodiment, the mobile electronic device is a telephone. Inan additional embodiment, the networked mobile electronic device is apersonal digital assistant. In a further embodiment, the mobileelectronic device is connected to the Internet.

In one embodiment the method for determining the geographic location ofa mobile electronic device uses the global positioning system. Inanother embodiment, the method for determining the geographic locationof a mobile electronic device is based upon estimation of the distancebetween the mobile electronic device and one or more antennas used toreceive radio signals from said mobile electronic device. In a furtherembodiment, the mobile electronic device communicates with a remoteserver.

In yet another embodiment, the variation of temperature setpoints islogged to a database. In a further embodiment, the variation oftemperature setpoints is initiated by a remote computer. In a differentembodiment, the temperature setpoints are varied automatically. In afurther embodiment, an occupant is prompted to confirm occupancy priorto adjustment of said temperature setpoint.

In one embodiment, a system for altering the setpoint on a thermostatfor space conditioning of a structure comprises at least one saidthermostat having at least one temperature setting associated with thepresence of one or more occupants in said structure, and at least onetemperature setting associated with the absence of occupants in saidstructure. The system also comprises one or more mobile electronicdevices having at least a user interface, where the mobile electronicdevices and said thermostat are connected to a network and where thesetpoint on said thermostat is adjusted between said temperature settingassociated with the presence of one or more occupants in said structureand said temperature setting associated with the absence of occupants insaid structure based upon the geographic location of said electronicdevice.

In a further embodiment, the mobile electronic device is a telephone. Inyet another embodiment, the networked mobile electronic device is apersonal digital assistant. In still a further embodiment, the mobileelectronic device is connected to the Internet.

In one embodiment, the method for determining the geographic location ofa mobile electronic device is the global positioning system. In anotherembodiment, the method for determining the geographic location of amobile electronic device is based upon estimation of the distancebetween the mobile electronic device and one or more antennas used toreceive radio signals from said mobile electronic device. In a furtherembodiment, the mobile electronic device communicates with a remoteserver.

In a different embodiment, the variation of temperature setpoints islogged to a database. In another embodiment, the variation oftemperature setpoints is initiated by a remote computer. In a furtherembodiment, the temperature setpoints are varied automatically. In stillanother embodiment, an occupant is prompted to confirm occupancy priorto adjustment of said temperature setpoint.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of an overall environment in which an embodimentof the invention may be used.

FIG. 2 shows a high-level illustration of the architecture of a networkshowing the relationship between the major elements of one embodiment ofthe subject invention.

FIGS. 3a and 3b show an embodiment of the website to be used as part ofthe subject invention.

FIG. 4 shows a high-level schematic of the thermostat used as part of anembodiment of the subject invention.

FIG. 5 shows one embodiment of the database structure used as part of anembodiment of the subject invention.

FIGS. 6a and 6b illustrate pages of a website that may be used with anembodiment of the subject invention.

FIG. 7 is a flowchart showing the steps involved in the operation of oneembodiment of the subject invention.

FIG. 8 is a flowchart that shows how the invention can be used to selectdifferent HVAC settings based upon its ability to identify which ofmultiple potential occupants is using the mobile device connected to thesystem.

FIGS. 9a and 9b show how comparing inside temperature against outsidetemperature and other variables permits calculation of dynamicsignatures.

FIG. 10 shows a flow chart for a high level version of the process ofcalculating the appropriate turn-on time in a given home.

FIG. 11 shows a more detailed flowchart listing the steps in the processof calculating the appropriate turn-on time in a given home for ajust-in-time event.

FIGS. 12a, 12b, 12c and 12d show the steps shown in the flowchart inFIG. 11 in the form of a graph of temperature and time.

FIG. 13 shows a table of some of the data used by an embodiment of thesubject invention to predict temperatures.

FIG. 14 shows an embodiment of the subject invention as applied in aspecific home on a specific day.

FIG. 15 shows an embodiment of the subject invention as applied in adifferent specific home on a specific day.

FIG. 16, which includes FIGS. 16-1 and 16-2, shows a table of predictedrates of change in temperature inside a given home for a range oftemperature differentials between inside and outside.

FIG. 17 shows how manual inputs can be recognized and recorded by anembodiment of the subject invention.

FIG. 18 shows how an embodiment of the subject invention uses manualinputs to interpret manual overrides and make short-term changes inresponse thereto.

FIG. 19 shows how an embodiment of the subject invention uses manualinputs to make long-term changes to interpretive rules and to setpointscheduling.

FIG. 20 shows a flowchart illustrating the steps required to initiate acompressor delay adjustment event.

FIGS. 21a, 21b, and 21c illustrate how changes in compressor delaysettings affect HVAC cycling behavior by plotting time againsttemperature.

FIG. 22 is a flow chart illustrating the steps involved in generating ademand reduction event for a given subscriber.

FIG. 23 is a flow chart illustrating the steps involved in confirmingthat a demand reduction event has taken place.

FIG. 24 is a representation of the movement of messages and informationbetween the components of an embodiment of the subject invention.

FIGS. 25a and 25b show graphical representations of inside and outsidetemperatures in two different homes, one with high thermal mass and onewith low thermal mass.

FIGS. 26a and 26b show graphical representations of inside and outsidetemperatures in the same homes as in FIGS. 25a and 25b , showing thecycling of the air conditioning systems in those houses.

FIGS. 27a and 27b show graphical representations of inside and outsidetemperatures in the same home as in FIGS. 25a and 26a , showing thecycling of the air conditioning on two different days in order todemonstrate the effect of a change in operating efficiency on theparameters measured by the thermostat.

FIGS. 28a and 28b show the effects of employing a pre-cooling strategyin two different houses.

FIGS. 29a and 29b show graphical representations of inside and outsidetemperatures in two different homes in order to demonstrate how thesystem can correct for erroneous readings in one house by referencingreadings in another.

FIG. 30 is a flowchart illustrating the steps involved in calculatingthe effective thermal mass of a home using an embodiment of the subjectinvention.

FIG. 31 is a flowchart illustrating the steps involved in determiningwhether an HVAC system has developed a problem that impairs efficiencyusing an embodiment of the subject invention.

FIG. 32 is a flowchart illustrating the steps involved in correcting forerroneous readings in one house by referencing readings in another usingan embodiment of the subject invention.

FIG. 33 shows the conventional programming of a programmable thermostatover a 24-hour period.

FIG. 34 shows the programming of a programmable thermostat over a24-hour period using ramped setpoints.

FIG. 35 shows the steps required for the core function of the rampedsetpoint algorithm.

FIG. 36 shows a flowchart listing steps in the process of decidingwhether to implement the ramped setpoint algorithm using an embodimentof the subject invention.

FIG. 37 shows the browser as seen on the display of the computer used aspart of an embodiment of the subject invention.

FIG. 38 is a flowchart showing the steps involved in the operation ofone embodiment of the subject invention.

FIG. 39 is a flowchart that shows how an embodiment of the invention canbe used to select different HVAC settings based upon its ability toidentify which of multiple potential occupants is using the computerattached to the system.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 1 shows an example of an overall environment 100 in which anembodiment of the invention may be used. The environment 100 includes aninteractive communication network 102 with computers 104 connectedthereto. Also connected to network 102 are mobile devices 105, and oneor more server computers 106, which store information and make theinformation available to computers 104 and mobile devices 105. Thenetwork 102 allows communication between and among the computers 104,mobile devices 105 and servers 106.

Presently preferred network 102 comprises a collection of interconnectedpublic and/or private networks that are linked to together by a set ofstandard protocols to form a distributed network. While network 102 isintended to refer to what is now commonly referred to as the Internet,it is also intended to encompass variations which may be made in thefuture, including changes additions to existing standard protocols. Italso includes various networks used to connect mobile and wirelessdevices, such as cellular networks.

When a user of an embodiment of the subject invention wishes to accessinformation on network 102 using computer 104, the user initiatesconnection from his computer 104. For example, the user invokes abrowser, which executes on computer 104. The browser, in turn,establishes a communication link with network 102. Once connected tonetwork 102, the user can direct the browser to access information onserver 106.

One popular part of the Internet is the World Wide Web. The World WideWeb contains a large number of computers 104 and servers 106, whichstore HyperText Markup Language (HTML) documents capable of displayinggraphical and textual information. HTML is a standard coding conventionand set of codes for attaching presentation and linking attributes toinformational content within documents.

The servers 106 that provide offerings on the World Wide Web aretypically called websites. A website is often defined by an Internetaddress that has an associated electronic page. Generally, an electronicpage is a document that organizes the presentation of text graphicalimages, audio and video.

In addition to the Internet, the network 102 can comprise a wide varietyof interactive communication media. For example, network 102 can includelocal area networks, interactive television networks, telephonenetworks, wireless data systems, two-way cable systems, and the like.

In one embodiment, computers 104 and servers 106 are conventionalcomputers that are equipped with communications hardware such as modemor a network interface card. The computers include processors such asthose sold by Intel and AMD. Other processors may also be used,including general-purpose processors, multi-chip processors, embeddedprocessors and the like.

Computers 104 can also be microprocessor-controlled home entertainmentequipment including advanced televisions, televisions paired with homeentertainment/media centers, and wireless remote controls.

Computers 104 may utilize a browser configured to interact with theWorld Wide Web. Such browsers may include Microsoft Explorer, Mozilla,Firefox, Opera or Safari. They may also include browsers or similarsoftware used on handheld, home entertainment and wireless devices.

The storage medium may comprise any method of storing information. Itmay comprise random access memory (RAM), electronically erasableprogrammable read only memory (EEPROM), read only memory (ROM), harddisk, floppy disk, CD-ROM, optical memory, or other method of storingdata.

Computers 104 and 106 may use an operating system such as MicrosoftWindows, Apple Mac OS, Linux, Unix or the like.

Computers 106 may include a range of devices that provide information,sound, graphics and text, and may use a variety of operating systems andsoftware optimized for distribution of content via networks.

Mobile devices 105 can also be handheld and wireless devices such aspersonal digital assistants (PDAs), cellular telephones and otherdevices capable of accessing the network. Mobile devices 105 can use avariety of means for establishing the location of each device at a giventime. Such methods may include the Global Positioning System (GPS),location relative to cellular towers, connection to specific wirelessaccess points, or other means

FIG. 2 illustrates in further detail the architecture of the specificcomponents connected to network 102 showing the relationship between themajor elements of one embodiment of the subject invention. Attached tothe network are thermostats 108 and computers 104 of various users.Connected to thermostats 108 are HVAC units 110. The HVAC units may beconventional air conditioners, heat pumps, or other devices fortransferring heat into or out of a building. Each user is connected tothe server 106 via wired or wireless connection such as Ethernet or awireless protocol such as IEEE 802.11, a gateway 110 that connects thecomputer and thermostat to the Internet via a broadband connection suchas a digital subscriber line (DSL), cellular radio or other form ofbroadband connection to the World Wide Web. Server 106 contains thecontent to be served as web pages and viewed by computers 104, softwareto manage thermostats 108, as well as databases containing informationused by the servers.

Also attached to the Network are cellular radio towers 120, or othermeans to transmit and receive wireless signals in communication withmobile devices 105. Such communication may use GPRS, GSM, CDMA, EvDO,EDGE or other protocols and technologies for connecting mobile devicesto a network.

FIGS. 3a and 3b shows a representative mobile device 105 from the frontand back, respectively. The front of the device includes a display 202,and may include a physical keyboard 204. Alternatively, a virtualkeyboard may be included in a display with touchscreen functionality.For devices with voice capabilities, such as a smart phone, a microphone206 and speaker 208 enable audio communications. An antenna 210, whichmay be mounted entirely within the mobile device, aids wirelesscommunications. Modules included within mobile device 105 but notvisible from the outside may include memory cards, microprocessors, GPSreceivers, battery, etc.

FIG. 4 shows a high-level block diagram of thermostat 108 used as partof an embodiment of the subject invention. Thermostat 108 includestemperature sensing means 252, which may be a thermistor, thermal diodeor other means commonly used in the design of electronic thermostats. Itincludes a microprocessor 254, memory 256, a display 258, a power source260, a relay 262, which turns the HVAC system on and off in response toa signal from the microprocessor, and contacts by which the relay isconnected to the wires that lead to the HVAC system.

To allow the thermostat to communicate bi-directionally with thecomputer network, the thermostat also includes means 264 to connect thethermostat to a local computer or to a wireless network. Such meanscould be in the form of Ethernet, wireless protocols such as IEEE802.11, IEEE 802.15.4, Bluetooth, cellular systems such as CDMA, GSM andGPRS, or other wireless protocols. The thermostat 250 may also includecontrols 266 allowing users to change settings directly at thethermostat, but such controls are not necessary to allow the thermostatto function.

The data used to manage the subject invention is stored on one or moreservers 106 within one or more databases. As shown in FIG. 5, theoverall database structure 300 may include temperature database 400,thermostat settings database 500, energy bill database 600, HVAChardware database 700, weather database 800, user database 900,transaction database 1000, product and service database 1100, userlocation database 1200 and such other databases as may be needed tosupport these and additional features.

Users of connected thermostats 250 may create personal accounts. Eachuser's account will store information in database 900, which tracksvarious attributes relative to users of the site. Such attributes mayinclude the make and model of the specific HVAC equipment in the user'shome; the age and square footage of the home, the solar orientation ofthe home, the location of the thermostat in the home, the user'spreferred temperature settings, whether the user is a participant in ademand response program, etc.

User personal accounts may also associate one or more mobile deviceswith such personal accounts. For mobile devices with the capability forgeopositioning awareness, these personal accounts will have the abilitylog such positioning data over time in database 1200.

In one embodiment, a background application installed on mobile device105 shares geopositioning data for the mobile device with theapplication running on server 106 that logs such data. Based upon thisdata, server 106 runs software that interprets said data (as describedin more detail below). Server 106 may then, depending on context, (a)transmit a signal to thermostat 108 changing setpoint because occupancyhas been detected at a time when the system did not expect occupancy; or(b) transmit a message to mobile device 105 that asks the user if theserver should change the current setpoint, alter the overall programmingof the system based upon a new occupancy pattern, etc. Such signallingactivity may be conducted via email, text message, pop-up alerts, voicemessaging, or other means.

FIGS. 6a and 6b illustrate a website that may be provided to assisthomeowners and others to interact with an embodiment of the subjectinvention. The website will permit thermostat users to perform throughthe web browser substantially all of the programming functionstraditionally performed directly at the physical thermostat, such astemperature set points, the time at which the thermostat should be ateach set point, etc. Preferably the website will also allow users toaccomplish more advanced tasks such as allow users to program invacation settings for times when the HVAC system may be turned off orrun at more economical settings, and to set macros that will allowchanging the settings of the temperature for all periods with a singlegesture such as a mouse click.

As shown in FIG. 6a , screen 351 of website 350 displays currenttemperature 352 as sensed by thermostat 108. Clicking on “up” arrow 354raises the setpoint 358; clicking the down arrow 356 lowers setpoint358. Screen 351 may also convey information about the outside weatherconditions, such as a graphic representation 360 of the sun, clouds,etc. In homes with multiple thermostats, screen 351 may allow users toselect different devices to adjust or monitor. Users will be able to usescreen 351 by selecting, for example, master bedroom thermostat 362,living room thermostat 364, game room thermostat 366, or basementthermostat 368.

As shown in FIG. 6b , screen 370 allows users to establish programmingschedules. Row 372 shows a 24-hour period. Programming row 374 displaysvarious programming periods and when they are scheduled, such as awaysetting 376, which begins at approximately 8 AM and runs untilapproximately 5:30 PM. When the away setting 376 is highlighted, theuser can adjust the starting time and ending time for the setting bydragging the beginning time 378 to the left to choose an earlier starttime, and dragging it to the right to make it later. Similarly, the usercan drag ending time 380 to the left to make it earlier, and to theright to make it later. While away setting 376 is highlighted, the usercan also change heating setpoint 382 by clicking on up arrow 384 or downarrow 386, and cooling setpoint 388 by clicking on up arrow 390 or downarrow 392. The user can save the program by clicking on save button 394.

FIG. 7 is a high-level flowchart showing the steps involved in theoperation of one embodiment of the subject invention. In step 1302,mobile device 105 transmits geopositioning information to server 106 viathe Internet. In step 1304 the server compares the latest geopositioningdata point to previous data points in order to determine whether achange in location or vector of movement has occurred. In step 1306 theserver evaluates the geopositioning data in order to determine whetherthe temperature settings for the HVAC system for the structureassociated with the mobile device 105 should be optimized for anunoccupied structure, or for an occupied structure in light of themovement (or lack thereof) in the geopositioning data.

If the server 106 determines that the home should be in occupied or“home” mode, then in step 1308 the server queries database 300 todetermine whether thermostat 108 is already set for home or away mode.If thermostat 108 is already in home mode, then the applicationterminates for a specified interval. If the HVAC settings then in effectare intended to apply when the home is unoccupied, then in step 1310 theapplication will retrieve from database 300 the user's specificpreferences for how to handle this situation.

If the user has previously specified (at the time that the program wasinitially set up or subsequently modified) that the user prefers thatthe system automatically change settings under such circumstances, theapplication then proceeds to step 1316, in which it changes theprogrammed setpoint for the thermostat to the setting intended for thehouse when occupied. If the user has previously specified that theapplication should not make such changes without further user input,then in step 1312 the application transmits a command to the locationspecified by the user (generally mobile device 105) directing the devicedisplay a message informing the user that the current setting assumes anunoccupied house and asking the user to choose whether to either keepthe current settings or revert to the pre-selected setting for anoccupied home.

If the user elects to retain the current setting, then in step 1318 theapplication will write to database 300 the fact that the user has soelected and terminate. If the user elects to change the setting, then instep 1316 the application transmits the revised setpoint to thethermostat. In step 1318 the application writes the updated settinginformation to database 300.

If the server 106 determines in step 1306 that the home should be inunoccupied or away mode, then in step 1350 the server queries database300 to determine whether thermostat 108 is set for set for home or awaymode. If thermostat 108 is already in away mode, then the applicationterminates for a specified interval. If the HVAC settings then in effectare intended to apply when the home is occupied, then in step 1352 theapplication will retrieve from database 300 the user's specificpreferences for how to handle this situation.

If the user has previously specified (at the time that the program wasinitially set up or subsequently modified) that the user prefers thatthe system automatically change settings under such circumstances, theapplication then proceeds to step 1358, in which it changes theprogrammed setpoint for the thermostat to the setting intended for thehouse when unoccupied. If the user has previously specified that theapplication should not make such changes without further user input,then in step 1354 the application transmits a command to the locationspecified by the user (generally mobile device 105) directing the devicedisplay a message informing the user that the current setting assumes anunoccupied house and asking the user to choose whether to either keepthe current settings or revert to the pre-selected setting for anoccupied home. If the user selects to retain the current setting, thenin step 1318 the application will write to database 300 the fact thatthe user has so elected and terminate.

If the user elects to change the setting, then in step 1316 theapplication transmits the revised setpoint to the thermostat. In step1318 the application writes the updated setting information to database300. If thermostat 108 is already in away mode, the program ends. If itwas in home mode, then in step 1314 server 108 initiates a state changeto put thermostat 108 in away mode. In either case, the server then instep 1316 writes the state change to database 300. In each case theserver can also send a message to the person who owns the mobile devicerequesting, confirming or announcing the state change.

FIG. 8 is a flowchart that shows one process by which an embodiment ofthe invention can be used to select different HVAC settings based uponits ability to identify which of multiple potential occupants is usingthe mobile device attached to the system. The process shown assumes (a)a static hierarchy of temperature preferences as between multipleoccupants: that is, that for a given home/structure, mobile user #1'spreferences will always control the outcome if mobile user #1 ispresent, that mobile user #2's preferences yield to #1's, but alwaysprevail over user #3, etc; and (b) that there are no occupants toconsider who are not associated with a mobile device. Other heuristicsmay be applied in order to account for more dynamic interactions ofpreferences.

In step 1402 server 106 retrieves the most recent geospatial coordinatesfrom the mobile device 105 associated with mobile user #1. In step 1404server 106 uses current and recent coordinates to determine whethermobile user #1's “home” settings should be applied. If server 106determines that User #1's home settings should be applied, then in step1406 server 106 applies the correct setting and transmits it to thethermostat(s).

In step 1408, server 106 writes to database 300 the geospatialinformation used to adjust the programming. If after performing step1404, the server concludes that mobile user #1's “home” settings shouldnot be applied, then in step 1412 server 106 retrieves the most recentgeospatial coordinates from the mobile device 105 associated with mobileuser #2.

In step 1414 server 106 uses current and recent coordinates to determinewhether mobile user #2's “home” settings should be applied. If server106 determines that User #2's home settings should be applied, then instep 1416 server 106 applies the correct setting and transmits it to thethermostat(s).

In step 1408, server 106 writes to database 300 the geospatial and otherrelevant information used to adjust the programming. If after performingstep 1414, the server concludes that mobile user #2's “home” settingsshould not be applied, then in step 1422 server 106 retrieves the mostrecent geospatial coordinates from the mobile device 105 associated withmobile user # N.

In step 1424 server 106 uses current and recent coordinates to determinewhether mobile user # N's “home” settings should be applied. If server106 determines that User # N's home settings should be applied, then instep 1426 server 106 applies the correct setting and transmits it to thethermostat(s). In step 1408, server 106 writes to database 300 thegeospatial information used to adjust the programming.

If none of the mobile devices associated with a given home or otherstructure report geospatial coordinates consistent with occupancy, thenin step 1430 the server instructs the thermostat(s) to switch to ormaintain the “away” setting.

One embodiment of the invention is capable of delivering additionalbenefits for homeowners in terms of increased comfort and efficiency. Inaddition to using the system to allow better signaling and control ofthe HVAC system, which relies primarily on communication running fromthe server to the thermostat, the bi-directional communication will alsoallow the thermostat 108 to regularly measure and send to the serverinformation about the temperature in the building. By comparing outsidetemperature, inside temperature, thermostat settings, cycling behaviorof the HVAC system, and other variables, the system will be capable ofnumerous diagnostic and controlling functions beyond those of a standardthermostat.

For example, FIG. 9a shows a graph of inside temperature, outsidetemperature and HVAC activity for a 24 hour period. When outsidetemperature 1502 increases, inside temperature 1504 follows, but withsome delay because of the thermal mass of the building, unless the airconditioning 1506 operates to counteract this effect. When the airconditioning turns on, the inside temperature stays constant (or risesat a much lower rate or even falls) despite the rising outsidetemperature. In this example, frequent and heavy use of the airconditioning results in only a very slight temperature increase insidethe house of 4 degrees, from 72 to 76 degrees, despite the increase inoutside temperature from 80 to 100 degrees.

FIG. 9b shows a graph of the same house on the same day, but assumesthat the air conditioning is turned off from noon to 7 PM. As expected,the inside temperature 1504 a rises with increasing outside temperatures1502 for most of that period, reaching 88 degrees at 7 PM. Becauseserver 106 logs the temperature readings from inside each house (whetheronce per minute or over some other interval), as well as the timing andduration of air conditioning cycles, database 300 will contain a historyof the thermal performance of each house.

The performance data will allow the server 106 to calculate an effectivethermal mass for each such structure—that is, the rate at which thetemperature inside a given building will change in response to changesin outside temperature. Because the server will also log these inputsagainst other inputs including time of day, humidity, etc., the serverwill be able to predict, at any given time on any given day, the rate atwhich inside temperature should change for given inside and outsidetemperatures.

The ability to predict the rate of change in inside temperature in agiven house under varying conditions may be applied by in effect holdingthe desired future inside temperature as a constraint and using theability to predict the rate of change to determine when the HVAC systemmust be turned on in order to reach the desired temperature at thedesired time. The ability of an HVAC system to vary turn-on time inorder to achieve a setpoint with minimum energy use may be thought of asJust In Time (JIT) optimization.

FIG. 10 shows a flowchart illustrating the high-level process forcontrolling a just-in-time (JIT) event. In step 1512, the serverdetermines whether a specific thermostat 108 is scheduled to run thepreconditioning program. If, not, the program terminates. If it soscheduled, then in step 1514 the server retrieves the predeterminedtarget time when the preconditioning is intended to have been completed(TT).

Using TT as an input, in step 1516 the server then determines the timeat which the computational steps required to program the preconditioningevent will be performed (ST). In step 1518, performed at start time ST,the server begins the process of actually calculating the requiredparameters, as discussed in greater detail below. Then in 1520 specificsetpoint changes are transmitted to the thermostat so that thetemperature inside the home may be appropriately changed as intended.

FIG. 11 shows a more detailed flowchart of the process. In step 1532,the server retrieves input parameters used to create a JIT event. Theseparameters include the maximum time allowed for a JIT event forthermostat 108 (MTI); the target time the system is intended to hit thedesired temperature (TT); and the desired inside temperature at TT(TempTT). It is useful to set a value for MTI because, for example, itwill be reasonable to prevent the HVAC system from running apreconditioning event if it would be expected to take 8 hours, whichmight be prohibitively expensive.

In step 1534, the server retrieves data used to calculate theappropriate start time with the given input parameters. This dataincludes a set of algorithmic learning data (ALD), composed of historicreadings from the thermostat, together with associated weather data,such as outside temperature, solar radiation, humidity, wind speed anddirection, etc; together with weather forecast data for the subjectlocation for the period when the algorithm is scheduled to run (theweather forecast data, or WFD). The forecasting data can be as simple asa listing of expected temperatures for a period of hours subsequent tothe time at which the calculations are performed, to more detailedtables including humidity, solar radiation, wind, etc. Alternatively, itcan include additional information such as some or all of the kinds ofdata collected in the ALD.

In step 1536, the server uses the ALD and the WFD to create predictiontables that determine the expected rate of change or slope of insidetemperature for each minute of HVAC cycle time (ΔT) for the relevantrange of possible pre-existing inside temperatures and outside climaticconditions. An example of a simple prediction table is illustrated inFIG. 13.

In step 1538, the server uses the prediction tables created in step1536, combined with input parameters TT and Temp(TT) to determine thetime at which slope ΔT intersects with predicted initial temperature PT.The time between PT and TT is the key calculated parameter: thepreconditioning time interval, or PTI.

In step 1540, the server checks to confirm that the time required toexecute the pre-conditioning event PTI does not exceed the maximumparameter MTI. If PTI exceeds MTI, the scheduling routine concludes andno ramping setpoints are transmitted to the thermostat.

If the system is perfect in its predictive abilities and its assumptionsabout the temperature inside the home are completely accurate, then intheory the thermostat can simply be reprogrammed once—at time PT, thethermostat can simply be reprogrammed to Temp(TT). However, there aredrawbacks to this approach.

First, if the server has been overly conservative in its predictions asto the possible rate of change in temperature caused by the HVAC system,the inside temperature will reach TT too soon, thus wasting energy andat least partially defeating the purpose of running the preconditioningroutine in the first place. If the server is too optimistic in itsprojections, there will be no way to catch up, and the home will notreach Temp(TT) until after TT. Thus it would be desirable to build intothe system a means for self-correcting for slightly conservative starttimes without excessive energy use.

Second, the use of setpoints as a proxy for actual inside temperaturesin the calculations is efficient, but can be inaccurate under certaincircumstances. In the winter (heating) context, for example, if theactual inside temperature is a few degrees above the setpoint (which canhappen when outside temperatures are warm enough that the home's natural“set point” is above the thermostat setting), then setting thethermostat to Temp(TT) at time PT will almost certainly lead to reachingTT too soon as well.

The currently preferred solution to both of these possible inaccuraciesis to calculate and program a series of intermediate settings betweenTemp(PT) and Temp(TT) that are roughly related to ΔT.

Thus if MTI is greater than PTI, then in step 1542 the server calculatesthe schedule of intermediate setpoints and time intervals to betransmitted to the thermostat. Because thermostats cannot generally beprogrammed with steps of less than 1 degree F., ΔT is quantized intodiscrete interval data of at least 1 degree F. each. For example, ifTemp(PT) is 65 degrees F., Temp(TT) is 72 degrees F., and PT is 90minutes, the thermostat might be programmed to be set at 66 for 10minutes, 67 for 12 minutes, 68 for 15 minutes, etc.

The server may optionally limit the process by assigning a minimumprogramming interval (e.g., at least ten minutes between setpointchanges) to avoid frequent switching of the HVAC system, which canreduce accuracy because of the thermostat's compressor delay circuit,which may prevent quick corrections. The duration of each individualstep may be a simple arithmetic function of the time PTI divided by thenumber of whole-degree steps to be taken; alternatively, the duration ofeach step may take into account second order thermodynamic effectsrelating to the increasing difficulty of “pushing” the temperatureinside a house further from its natural setpoint given outside weatherconditions, etc. (that is, the fact that on a cold winter day it maytake more energy to move the temperature inside the home from 70 degreesF. to 71 than it does to move it from 60 degrees to 61).

In step 1544, the server schedules setpoint changes calculated in step1542 for execution by the thermostat.

With this system, if actual inside temperature at PT is significantlyhigher than Temp(PT), then the first changes to setpoints will have noeffect (that is, the HVAC system will remain off), and the HVAC systemwill not begin using energy, until the appropriate time, as shown inFIG. 12. Similarly, if the server has used conservative predictions togenerate ΔT, and the HVAC system runs ahead of the predicted rate ofchange, the incremental changes in setpoint will delay further increasesuntil the appropriate time in order to again minimize unnecessary energyuse, as shown in FIG. 11.

FIG. 12(a) through 12(d) shows the steps in the preconditioning processas a graph of temperature and time. FIG. 12(a) shows step 1532, in whichinputs target time TT 1552, target temperature Temp(TT) 1554, maximumconditioning interval MTI 1556 and the predicted inside temperatureduring the period of time the preconditioning event is likely to beginTemp(PT) 1558 are retrieved.

FIG. 12(b) shows the initial calculations performed in step 1538, inwhich expected rate of change in temperature ΔT 1560 inside the home isgenerated from the ALD and WFD using Temp(TT) 1554 at time TT 1552 asthe endpoint.

FIG. 12(c) shows how in step 1538 ΔT 1560 is used to determine starttime PT 1562 and preconditioning time interval PTI 1564. It also showshow in step 1540 the server can compare PTI with MTI to determinewhether or not to instantiate the pre-conditioning program for thethermostat.

FIG. 12(d) shows step 1542, in which specific ramped setpoints 1566 aregenerated. Because of the assumed thermal mass of the system, actualinside temperature at any given time will not correspond to setpointsuntil some interval after each setpoint change. Thus initial rampedsetpoint 1216 may be higher than Temp(PT) 1558, for example.

FIG. 13 shows an example of the types of data that may be used by theserver in order to calculate ΔT 1560. Such data may include insidetemperature 1572, outside temperature 1574, cloud cover 1576, humidity1578, barometric pressure 1580, wind speed 1582, and wind direction1584.

Each of these data points should be captured at frequent intervals. Inthe preferred embodiment, as shown in FIG. 13, the interval is onceevery 60 seconds.

FIG. 14 shows application of an embodiment of the subject invention inan actual house. Temperature and setpoints are plotted for the 4-hourperiod from 4 AM to 8 AM with temperature on the vertical axis and timeon the horizontal axis. The winter nighttime setpoint 1592 is 60 degreesF.; the morning setpoint temperature 1594 is 69 degrees F. The outsidetemperature 1596 is approximately 45 degrees F. The target time TT 1598for the setpoint change to morning setting is 6:45 AM. In the absence ofembodiments of the subject invention, the homeowner could program thethermostat to change to the new setpoint at 6:45, but there is aninherent delay between a setpoint change and the response of thetemperature inside the home. (In this home on this day, the delay isapproximately fifty minutes.) Thus if the homeowner truly desired toachieve the target temperature at the target time, some anticipationwould be necessary. The amount of anticipation required depends uponnumerous variables, as discussed above.

After calculating the appropriate slope ΔT 1560 by which to ramp insidetemperature in order to reach the target as explained above, the servertransmits a series of setpoints 1566 to the thermostat because thethermostat is presumed to only accept discrete integers as programsettings. (If a thermostat is capable of accepting finer settings, as inthe case of some thermostats designed to operate in regions in whichtemperature is generally denoted in Centigrade rather than Fahrenheit,which accept settings in half-degree increments, tighter control may bepossible.)

In any event, in the currently preferred embodiment of the subjectinvention, programming changes are quantized such that the frequency ofsetpoint changes is balanced between the goal of minimizing networktraffic and the frequency of changes made on the one hand and the desirefor accuracy on the other. Balancing these considerations may result insome cases in either more frequent changes or in larger steps betweensettings. As shown in FIG. 14, the setpoint “stairsteps” from 60 degreesF. to 69 degrees F. in nine separate setpoint changes over a period of90 minutes.

Because the inside temperature 1599 when the setpoint management routinewas instantiated at 5:04 AM was above the “slope” and thus above thesetpoint, the HVAC system was not triggered and no energy was usedunnecessarily heating the home before such energy use was required.Actual energy usage does not begin until 5:49 AM.

FIG. 15 shows application of an embodiment of the subject invention in adifferent house during a similar four hour interval. In FIG. 15, thepredicted slope ΔT 1560 is less conservative relative to the actualperformance of the home and HVAC system, so there is no off cyclingduring the preconditioning event—the HVAC system turns on atapproximately 4:35 AM and stays on continuously during the event. Thehome reaches the target temperature Temp(TT) roughly two minutes priorto target time TT.

FIG. 16 shows a simple prediction table. The first column 1602 lists aseries of differentials between outside and inside temperatures. Thuswhen the outside temperature is 14 degrees and the inside temperature is68 degrees, the differential is −54 degrees; when the outsidetemperature is 94 degrees and the inside temperature is 71 degrees, thedifferential is 13 degrees. The second column 1604 lists the predictedrate of change in inside temperature ΔT 1210 assuming that the furnaceis running in terms of degrees Fahrenheit of change per hour. A similarprediction table will be generated for predicted rates of change whenthe air conditioner is on; additional tables may be generated thatpredict how temperatures will change when the HVAC system is off.

Alternatively, the programming of the just-in-time setpoints may bebased not on a single rate of change for the entire event, but on a morecomplex multivariate equation that takes into account the possibilitythat the rate of change may be different for events of differentdurations.

The method for calculating start times may also optionally take intoaccount not only the predicted temperature at the calculated start time,but may incorporate measured inside temperature data from immediatelyprior to the scheduled start time in order to update calculations, ormay employ more predictive means to extrapolate what inside temperaturebased upon outside temperatures, etc.

An additional capability offered by an embodiment of the instantinvention is the ability to adapt the programming of the HVAC controlsystem based upon the natural behavior of occupants. Because anembodiment of the instant invention is capable of recording the setpointactually used at a connected thermostat over time, it is also capable ofinferring manual setpoint changes (as, for example, entered by pushingthe “up” or “down” arrow on the control panel of the device) even whensuch overrides of the pre-set program are not specifically recorded assuch by the thermostat.

In order to adapt programming to take into account the manual overridesentered into the thermostat, it is first necessary to determine when amanual override has in fact occurred. Most thermostats, includingtwo-way communicating devices discussed herein, do not record suchinputs locally, and neither recognize nor transmit the fact that amanual override has occurred. Furthermore, in a system as describedherein, frequent changes in setpoints may be initiated by algorithmsrunning on the server, thereby making it impossible to infer a manualoverride from the mere fact that the setpoint has changed. It istherefore necessary to deduce the occurrence of such events from thedata that an embodiment of the subject invention does have access to.

FIG. 17 illustrates the currently preferred method for detecting theoccurrence of a manual override event. In step 1702, the serverretrieves the primary data points used to infer the occurrence of amanual override from one or more databases in overall database structure300. The data should include each of the following: for the most recentpoint at which it can obtain such data (time0) the actual setpoint asrecorded at the thermostat at (A0); for the point immediately prior totime0 (time-1), the actual setpoint recorded for the thermostat (A-1);for time0 the setpoint as scheduled by server 106 according to the basicsetpoint programming (S0), and for time-1 the setpoint as scheduled byserver 106 according to the standard setpoint programming (S-1).

In step 1704, the server retrieves any additional automated setpointchanges C that have been scheduled for the thermostat by server 106 attime0. Such changes may include algorithmic changes intended to reduceenergy consumption, etc.

In step 1706 the server calculates the difference (dA) between A0 andA-1; for example, if the actual setpoint is 67 degrees at T-1 and 69 atT0, dA is +2; if the setpoint at T-1 is 70 and the setpoint at T0 is 66,dA is −4.

In step 1708, the server performs similar steps in order to calculatedS, the difference between S0 and S-1. This is necessary because, forexample, the setpoint may have been changed because the server itselfhad just executed a change, such as a scheduled change from “away” to“home” mode. In step 1710 the server evaluates and sums all activealgorithms and other server-initiated strategies to determine their neteffect on setpoint at time0. For example, if one algorithm has increasedsetpoint at time0 by 2 degrees as a short-term energy savings measure,but another algorithm has decreased the setpoint by one degree tocompensate for expected subjective reactions to weather conditions, thenet algorithmic effect sC is +1 degree.

In step 1712, the server calculates the value for M, where M is equal tothe difference between actual setpoints dA, less the difference betweenscheduled setpoints dS, less the aggregate of algorithmic change sC.

In step 1714 the server evaluates this difference. If the differenceequals zero, the server concludes that no manual override has occurred,and the routine terminates. But if the difference is any value otherthan zero, then the server concludes that a manual override hasoccurred. Thus in step 1716 the server logs the occurrence and magnitudeof the override to one or more databases in overall database structure300.

The process of interpreting a manual override is shown in FIG. 18. Step1802 is the detection of an override, as described in detail in FIG. 17.In step 1804 the server retrieves the stored rules for the subjectthermostat 108. Such rules may include weather and time-relatedinferences such as “if outside temperature is greater than 85 degreesand inside temperature is more than 2 degrees above setpoint and manualoverride lowers setpoint by 3 or more degrees, then revert to originalsetpoint in 2 hours,” or “if heating setpoint change is scheduled from“away” to “home” within 2 hours after detected override, and overrideincreases setpoint by at least 2 degrees, then change to “home”setting,” or the like.

In step 1806 the server retrieves contextual data required to interpretthe manual override. Such data may include current and recent weatherconditions, current and recent inside temperatures, etc. This data ishelpful because it is likely that manual overrides are at least in partdeterministic: that is, that they may often be explained by suchcontextual data, and that such understanding can permit anticipation ofthe desire on the part of the occupants to override and to adjustprogramming accordingly, so as to anticipate and obviate the need forsuch changes. The amount of data may be for a period of a few hours toas long as several days or more. Recent data may be more heavilyweighted than older data in order to assure rapid adaptation tosituations in which manual overrides represent stable changes such aschanges in work schedules, etc.

In step 1808 the server retrieves any relevant override data from theperiod preceding the specific override being evaluated that has not yetbeen evaluated by and incorporated into the long-term programming andrules engines as described below in FIG. 19. In step 1810 the serverevaluates the override and determines which rule, if any, should beapplied as a result of the override.

In step 1812 the server determines whether to alter the current setpointas a result of applying the rules in step 1810. If no setpoint change isindicated, then the routine ends. If a setpoint change is indicated,then in step 1814 the server transmits the setpoint change to thethermostat for execution in the home, and in step 1816 it records thatchange to one or more databases in overall database structure 300.

In order to ensure that both the stored rules for interpreting manualoverrides and the programming itself continue to most accurately reflectthe intentions of the occupants, the server will periodically reviewboth the rules used to interpret overrides and the setpoint schedulingemployed. FIG. 19 shows the steps used to incorporate manual overridesinto the long-term rules and setpoint schedule. In step 1902 the serverretrieves the stored programming for a given thermostat as well as therules for interpreting overrides for that thermostat.

In step 1904 the server retrieves the recent override data as recordedin FIGS. 17 and 18 to be evaluated for possible revisions to the rulesand the programming. In step 1906 the server retrieves the contextualdata regarding overrides retrieved in step 1904 (Because the processillustrated in FIG. 19 is not presently expected to be executed as areal-time process, and is expected to be run anywhere from once per dayto once per month, the range and volume of contextual data to beevaluated is likely to be greater than in the process illustrated inFIG. 18.

In step 1908 the server interprets the overrides in light of theexisting programming schedule, rules for overrides, contextual data,etc. In step 1910 the server determines whether, as a result of thoseoverrides as interpreted, the rules for interpreting manual overridesshould be revised. If the rules are not to be revised, the server movesto step 1914. If the rules are to be revised, then in step 1912 theserver revises the rules and the new rules are stored in one or moredatabases in overall database structure 300.

In step 1914 the server determines whether any changes to the baselineprogramming for the thermostat should be revised. If not, the routineterminates. If revisions are warranted, then in step 1916 the serverretrieves from database 900 the permissions the server has to makeautonomous changes to settings. If the server has been given permissionto make the proposed changes, then in step 1918 the server revises thethermostat's programming and writes the changes to one or more databasesin overall database structure 300.

If the server has not been authorized to make such changes autonomously,then in step 1920 the server transmits the recommendation to changesettings to the customer in the manner previously specified by thecustomer, such as email, text message, personalized website, etc.

Additional means of implementing an embodiment of the instant inventionmay be achieved using variations in system architecture. For example,much or even all of the work being accomplished by remote server 106 mayalso be done by thermostat 108 if that device has sufficient processingcapabilities, memory, etc. Alternatively, these steps may be undertakenby a local processor such as a local personal computer, or by adedicated appliance having the requisite capabilities, such as gateway112.

An additional way in which an embodiment of the instant invention canreduce energy consumption with minimal impact on comfort is to vary theturn-on delay enforced by the thermostat after the compressor is turnedoff. Compressor delay is usually used to protect compressors from rapidcycling, which can physically damage them.

The ability to predict the rate of change in inside temperature in agiven house under varying conditions may also be applied to permitcalculation of the effect of different compressor delay settings oninside temperatures, HVAC cycling and energy consumption.

FIG. 20 shows a flowchart illustrating the steps required to initiate acompressor delay adjustment event. In step 2002, server 106 retrievesparameters such as weather conditions, the current price perkilowatt-hour of electricity, and the state of the electric grid interms of supply versus demand for the geographic area that includes agiven home. In step 2004 server 106 determines whether to instantiatethe compressor delay adjustment program for a certain group of homes inresponse to those conditions.

In step 2006, server 106 determines whether a specific home issubscribed to participate in compressor delay events. If a given home iseligible, then in step 2008 the server retrieves the parameters neededto specify the compressor delay routine for that home. These may includeuser preferences, such as the weather, time of day and other conditionsunder which the homeowner has elected to permit hysteresis band changes,the maximum length of compressor delay authorized, etc.

In step 2010 the appropriate compressor delay settings are determined,and in step 2012 the chosen settings are communicated to the thermostat.In step 2014 the server determines if additional thermostats in thegiven group must still be evaluated. If so, the server returns to step2006 and repeats the subsequent steps with the next thermostat. If not,the routine ends.

FIGS. 21(a) through 21(c) illustrate how changes in compressor delaysettings affect HVAC cycling behavior by plotting time againsttemperature. In FIG. 21(a), time is shown on the horizontal axis 2102,and temperature is shown on vertical axis 2104. The setpoint forthermostat 108 is 70 degrees F., which results in the cycling behaviorshown for inside temperature 2106. Because compressor delay CD1 2108 is,at approximately 3 minutes, shorter than the natural duration of acompressor off cycle Off1 2110 at approximately 6 minutes for thisparticular house under the illustrated conditions, the compressor delayhas no effect on the operation of the HVAC system.

Because the hysteresis band operates to so as to maintain thetemperature within a range of plus or minus one degree of the setpoint,in the case of air conditioning the air conditioner will switch on whenthe inside temperature reaches 71 degrees, continue operating until itreaches 69 degrees, then shut off. The system will then remain off untilit reaches 71 degrees again, at which time it will again switch on. Thepercentage of time during which inside temperature is above or below thesetpoint will depend on conditions and the dynamic signature of theindividual, home. Under the conditions illustrated, the average insidetemperature ΔT1 2112 is roughly equal to the setpoint of 70 degrees.

FIG. 21(b) shows how with the same environmental conditions as in FIG.21(a), the cycling behavior of the inside temperature changes when thecompressor delay is longer than the natural compressor off cycle Off12110. Extended compressor delay CD2 2114 allows inside temperature 2116to climb above the range normally enforced by the hysteresis band.Because CD2 is roughly 8 minutes, under the given conditions the insidetemperature climbs to approximately 72 degrees before the compressordelay allows the air conditioner to restart and drive the insidetemperature back down. But as before, the air conditioner shuts off whenthe inside temperature reaches 69 degrees. Thus the average temperatureis increased from ΔT1 2112 to ΔT2 2118. This change will save energy andreduce cycling because it takes less energy to maintain a higher insidetemperature with an air conditioner. However, the setpoint reported bythe display of the thermostat will continue to be the occupant's chosensetpoint of 70 degrees.

FIG. 21(c) shows how the same compressor delay can result in differentthermal cycling with different weather conditions. The greater theamount by which outside temperature exceeds inside temperature in theair conditioning context, the more rapidly the inside temperature willincrease during an off cycle, and the slower the air conditioner will beable to cool during the on cycle. Thus as compared to FIG. 21(b), whenthe inside temperature increased to roughly 72 degrees during theextended compressor delay of 8 minutes, a higher outside temperaturewill cause the inside temperature to increase faster, which results in apeak temperature of roughly 73 degrees, and in wider temperature cycling2120. The average inside temperature consequently increases from ΔT(2)2118 to ΔT(3) 2122.

It should be noted that the shape of the actual waveform will mostlikely not be sinusoidal, but for ease of illustration it is sometimesbe presented as such in the figures.

Residential air conditioning is a major component of peak load. Thetraditional approach to dealing with high demand on hot days is toincrease supply—build new powerplants, or buy additional capacity on thespot market. But because reducing loads has come to be considered bymany to be a superior strategy for matching electricity supply to demandwhen the grid is stressed, the ability to shed load by turning off airconditioners during peak events has become a useful tool for managingloads. A key component of any such system is the ability to document andverify that a given air conditioner has actually turned off. Datalogging hardware can accomplish this, but due to the cost is usuallyonly deployed for statistical sampling. An embodiment of the instantinvention provides a means to verify demand response without additionalhardware such as a data logger.

Because server 106 logs the temperature readings from inside each house(whether once per minute or over some other interval), as well as thetiming and duration of air conditioning cycles, database 300 willcontain a history of the thermal performance of each house. Thatperformance data will allow the server 106 to calculate an effectivethermal mass for each such structure—that is, the speed with thetemperature inside a given building will change in response to changesin outside temperature. Because the server will also log these inputsagainst other inputs including time of day, humidity, etc. the serverwill be able to predict, at any given time on any given day, the rate atwhich inside temperature should change for given inside and outsidetemperatures. This will permit remote verification of load shedding bythe air conditioner without directly measuring or recording theelectrical load drawn by the air conditioner.

FIG. 22 shows the steps followed in order to initiate air conditionershutoff. When a summer peak demand situation occurs, the utility willtransmit an email 2202 or other signal to server 106 requesting areduction in load. Server 106 will determine 2204 if the user's house isserved by the utility seeking reduction; determine 2206 if a given userhas agreed to reduce peak demand; and determine 2208 if a reduction ofconsumption by the user is required or desirable in order to achieve thereduction in demand requested by the utility. The server will transmit2210 a signal to the user's thermostat 108 signaling the thermostat toshut off the air conditioner 110.

FIG. 23 shows the steps followed in order to verify that the airconditioner has in fact been shut off. Server 106 will receive andmonitor 2302 the temperature readings sent by the user's thermostat 108.The server then calculates 2304 the temperature reading to be expectedfor that thermostat given inputs such as current and recent outsidetemperature, recent inside temperature readings, the calculated thermalmass of the structure, temperature readings in other houses, etc. Theserver will compare 2306 the predicted reading with the actual reading.If the server determines that the temperature inside the house is risingat the rate predicted if the air conditioning is shut off, then theserver confirms 2308 that the air conditioning has been shut off. If thetemperature reading from the thermostat shows no increase, orsignificantly less increase than predicted by the model, then the serverconcludes 2310 that the air conditioning was not switched off, and thatno contribution to the demand response request was made.

For example, assume that on at 3 PM on date Y utility X wishes totrigger a demand reduction event. A server at utility X transmits amessage to the server at demand reduction service provider Z requestingW megawatts of demand reduction. The demand reduction service providerserver determines that it will turn off the air conditioner at house Ain order to achieve the required demand reduction. At the time the eventis triggered, the inside temperature as reported by the thermostat inhouse A is 72 degrees F. The outside temperature near house A is 96degrees Fahrenheit. The inside temperature at House B, which is not partof the demand reduction program, but is both connected to the demandreduction service server and located geographically proximate to HouseA, is 74 F. Because the air conditioner in house A has been turned off,the temperature inside House A begins to rise, so that at 4 PM it hasincreased to 79 F. Because the server is aware of the outsidetemperature, which remains at 96 F, and of the rate of temperature riseinside house A on previous days on which temperatures have been at ornear 96 F, and the temperature in house B, which has risen only to 75 Fbecause the air conditioning in house B continues to operate normally,the server is able to confirm with a high degree of certainty that theair conditioner in house A has indeed been shut off.

In contrast, if the HVAC system at house A has been tampered with, sothat a demand reduction signal from the server does not actually resultin shutting off the air conditioner in house A, when the server comparesthe rate of temperature change at house A against the other data points,the server will receive data inconsistent with the rate of increasepredicted. As a result, it will conclude that the air conditioner hasnot been shut off in house A as expected, and may not credit house Awith the financial credit that would be associated with demand reductioncompliance, or may trigger a business process that could result intermination of house A's participation in the demand reduction program.

FIG. 24 illustrates the movement of signals and information between thecomponents of one embodiment of the subject invention to trigger andverify a demand reduction response. Where demand response events areundertaken on behalf of a utility by a third party, participants in thecommunications may include electric utility server 2400, demandreduction service server 106, and thermostat 108. In step 2402 theelectric utility server 2400 transmits a message to demand reductionservice server 106 requesting a demand reduction of a specified durationand size. Demand reduction service server 106 uses database 300 todetermine which subscribers should be included in the demand reductionevent. For each included subscriber, the server then sends a signal 2404to the subscriber's thermostat 108 instructing it (a) to shut down atthe appropriate time or (b) to allow the temperature as measured by thethermostat to increase to a certain temperature at the specified time,depending upon the agreement between the homeowner and the utilityand/or demand reduction aggregator.

The server then receives 2406 temperature signals from the subscriber'sthermostat. At the conclusion of the demand reduction event, the servertransmits a signal 2408 to the thermostat permitting the thermostat tosignal its attached HVAC system to resume cooling, if the system hasbeen shut off, or to reduce the target temperature to its pre-demandreduction setting, if the target temperature was merely increased. Ifthermostat 108 is capable of storing scheduling information, theseinstructions may be transmitted prior to the time they are to beexecuted and stored locally. After determining the total number ofsubscribers actually participating in the DR event, the server thencalculates the total demand reduction achieved and sends a message 2410to the electric utility confirming such reduction.

Additional steps may be included in the process. For example, if thesubscriber has previously requested that notice be provided when a peakdemand reduction event occurs, the server will also send an alert, whichmay be in the form of an email or text message or an update to thepersonalized web page for that user, or both. If the server determinesthat a given home has (or has not) complied with the terms of its demandreduction agreement, the server may send a message to the subscriberconfirming that fact.

It should also be noted that in some climate zones, peak demand eventsoccur during extreme cold weather rather than (or in addition to) duringhot weather. The same process as discussed above could be employed toreduce demand by shutting off electric heaters and monitoring the rateat which temperatures fall.

It should also be noted that the peak demand reduction service can beperformed directly by an electric utility, so that the functions ofserver 106 can be combined with the functions of server 2400.

The system installed in a subscriber's home may optionally includeadditional temperature sensors at different locations within thebuilding. These additional sensors may we connected to the rest of thesystem via a wireless system such as 802.11 or 802.15.4, or may beconnected via wires. Additional temperature and/or humidity sensors mayallow increased accuracy of the system, which can in turn increase usercomfort, energy savings or both.

The bi-directional communication between server 106 and thermostat 108will also allow thermostat 108 to regularly measure and send to server106 information about the temperature in the building. By comparingoutside temperature, inside temperature, thermostat settings, cyclingbehavior of the HVAC system, and other variables, the system will becapable of numerous diagnostic and controlling functions beyond those ofa standard thermostat.

For example, FIG. 25a shows a graph of inside temperature and outsidetemperature for a 24-hour period in House A, assuming no HVAC activity.House A has double-glazed windows and is well-insulated. When outsidetemperature 2502 increases, inside temperature 2504 follows, but withsignificant delay because of the thermal mass of the building.

FIG. 25b shows a graph of inside temperature and outside temperature forthe same 24-hour period in House B. House B is identical to House Aexcept that it (i) is located a block away and (ii) has single-glazedwindows and is poorly insulated. Because the two houses are so close toeach other, outside temperature 2502 is the same in FIG. 25a and FIG.25b . But the lower thermal mass of House B means that the rate at whichthe inside temperature 2506 changes in response to the changes inoutside temperature is much greater.

The differences in thermal mass will affect the cycling behavior of theHVAC systems in the two houses as well. FIG. 26a shows a graph of insidetemperature and outside temperature in House A for the same 24-hourperiod as shown in FIG. 6a , but assuming that the air conditioning isbeing used to try to maintain an internal temperature of 70 degrees.Outside temperatures 2502 are the same as in FIGS. 25a and 25b . Insidetemperature 2608 is maintained within the range determined by thermostat108 by the cycling of the air conditioner. Because of the high thermalmass of the house, the air conditioning does not need to run for verylong to maintain the target temperature, as shown by shaded areas 2610.

FIG. 26b shows a graph of inside temperature 2612 and outsidetemperature 2502 for the same 24-hour period in House B, assuming use ofthe air conditioning as in FIG. 26a . Because of the lower thermal massof House B, the air conditioning system in House B has to run longer inorder to maintain the same target temperature range, as shown by shadedareas 2614.

Because server 106 logs the temperature readings from inside each house(whether once per minute or over some other interval), as well as thetiming and duration of air conditioning cycles, database 300 willcontain a history of the thermal performance of each house. Thatperformance data will allow the server 106 to calculate an effectivethermal mass for each such structure—that is, the speed with thetemperature inside a given building will change in response to changesin outside temperature and differences between inside and outsidetemperatures. Because the server 106 will also log these inputs againstother inputs including time of day, humidity, etc. the server will beable to predict, at any given time on any given day, the rate at whichinside temperature should change for given inside and outsidetemperatures.

The server will also record the responses of each house to changes inoutside conditions and cycling behavior over time. That will allow theserver to diagnose problems as and when they develop. For example, FIG.27a shows a graph of outside temperature 2702, inside temperature 2704and HVAC cycle times 2706 in House A for a specific 24-hour period ondate X. Assume that, based upon comparison of the performance of House Aon date X relative to House A's historical performance, and incomparison to the performance of House A relative to other nearby houseson date X, the HVAC system in House A is presumed to be operating atnormal efficiency, and that House A is in the 86th percentile ascompared to those other houses.

FIG. 27b shows a graph of outside temperature 2708, inside temperature2710 and HVAC cycle times 2712 in House A for the 24-hour period on dateX+1. House A's HVAC system now requires significantly longer cycle timesin order to try to maintain the same internal temperature. If thoselonger cycle times were due to higher outside temperatures, those cycletimes would not indicate the existence of any problems. But becauseserver 106 is aware of the outside temperature, the system can eliminatethat possibility as an explanation for the higher cycle times.

Because server 106 is aware of the cycle times in nearby houses, it candetermine that, for example, on date X+1 the efficiency of House A isonly in the 23rd percentile. The server will be programmed with a seriesof heuristics, gathered from predictive models and past experience,correlating the drop in efficiency and the time interval over which ithas occurred with different possible causes. For example, a 50% drop inefficiency in one day may be correlated with a refrigerant leak,especially if followed by a further drop in efficiency on the followingday. A reduction of 10% over three months may be correlated with aclogged filter. Based upon the historical data recorded by the server,the server 106 will be able to alert the homeowner that there is aproblem and suggest a possible cause.

Because the system will be able to calculate effective thermal mass, itwill be able to determine the cost effectiveness of strategies such aspre-cooling for specific houses under different conditions. FIG. 28ashows a graph of outside temperature 2802, inside temperature 2804 andHVAC cycling times 2806 in House A for a specific 24-hour period on dateY assuming that the system has used a pre-cooling strategy to avoidrunning the air conditioning during the afternoon, when rates arehighest. Because House A has high thermal mass, the house is capable of“banking” cooling, and energy consumed during off-peak hours is ineffect stored, allowing the house to remain cool even when the system isturned off. Temperatures keep rising during the period the airconditioning is off, but because thermal mass is high, the rate ofincrease is low, and the house is still comfortable six hours later.

Although the pre-cooling cycle time in a given home may be relativelylong, the homeowner may still benefit if the price per kilowatt duringthe morning pre-cooling phase is lower than the price during the peakload period. FIG. 28b shows a graph of the same outside temperature 2802in House B as in House A in FIG. 28a for the same 24-hour period andusing the same pre-cooling strategy as shown by cycling times 2806. Butbecause House B has minimal thermal mass, using additional electricityin order to pre-cool the house does not have the desired effect; insidetemperature 2808 warms up so fast that the cooling that had been bankedis quickly lost. Thus the system will recommend that House A pre-cool inorder to save money, but not recommend pre-cooling for House B.

The system can also help compensate for anomalies such as measurementinaccuracies due to factors such as poor thermostat location. It is wellknown that thermostats should be placed in a location that will belikely to experience “average” temperatures for the overall structure,and should be isolated from windows and other influences that could biasthe temperatures they “see.” But for various reasons, not all thermostatinstallations fit that ideal. FIG. 29a shows a graph of outsidetemperature 2902, the actual average inside temperature for the entirehouse 2904, and inside temperature as read by the thermostat 2906 inHouse C for a specific 24-hour period on September 15th, assuming thatthe thermostat is located so that for part of the afternoon on that daythe thermostat is in direct sunlight.

Until the point at which the sun hits the thermostat, the average insidetemperature and temperature as read by the thermostat track veryclosely. But when the direct sunlight hits the thermostat, thethermostat and the surrounding area can heat up, causing the internaltemperature as read by the thermostat to diverge significantly from theaverage temperature for the rest of the house. A conventional thermostathas no way of distinguishing this circumstance from a genuinely hot day,and will both over-cool the rest of the house and waste considerableenergy when it cycles the air conditioner in order to reduce thetemperature as sensed by the thermostat.

If the air conditioning is turned off, this phenomenon will manifest asa spike in temperature as measured by the thermostat. If the airconditioning is turned on (and has sufficient capacity to respond to thedistorted temperature signal caused by the sunlight), this phenomenonwill likely manifest as relatively small changes in the temperature assensed by the thermostat, but significantly increased HVAC usage (aswell as excessively lowered temperatures in the rest of the house, butthis result may not be directly measured in a single sensorenvironment).

An embodiment of the system, in contrast, has multiple mechanisms thatwill allow it to correct for such distortions. First, because anembodiment of the subject system compares the internal readings fromHouse C with the external temperature, it will be obvious that the risein temperature at 4:00 PM is not correlated with a corresponding changein outside temperature. Second, because the system is also monitoringthe readings from the thermostat in nearby House D, which (as shown inFIG. 29b ) is exposed to the same outside temperature 602, but has nosudden rise in measured internal afternoon temperature 2908, the systemhas further validation that the temperature increase is not caused byclimatic conditions. And finally, because the system has monitored andrecorded the temperature readings from the thermostat in House C foreach previous day, and has compared the changing times of the aberrationwith the progression of the sun, the system can distinguish the patternslikely to indicate solar overheating from other potential causes.

FIG. 30 illustrates the steps involved in calculating comparativethermal mass, or the thermal mass index. In step 3002, the serverretrieves climate data related to home X. Such data may include currentoutside temperature, outside temperature during the preceding hours,outside humidity, wind direction and speed, whether the sun is obscuredby clouds, and other factors. In step 3004, the server retrieves HVACduty cycle data for home X. Such data may include target settings set bythe thermostat in current and previous periods, the timing of switch-onand switch-off events and other data.

In step 3006, the server retrieves data regarding recent temperaturereadings as recorded by the thermostat in home X. In step 3008, theserver retrieves profile data for home X. Such data may include squarefootage and number of floors, when the house was built and/or renovated,the extent to which it is insulated, its address, make, model and age ofits furnace and air conditioning hardware, and other data.

In step 3010, the server retrieves the current inside temperaturereading as transmitted by the thermostat. In step 3012, the servercalculates the thermal mass index for the home under those conditions;that is, for example, it calculates the likely rate of change forinternal temperature in home X from a starting point of 70 degrees whenthe outside temperature is 85 degrees at 3:00 PM on August 10th when thewind is blowing at 5 mph from the north and the sky is cloudy. Theserver accomplishes this by applying a basic algorithm that weighs eachof these external variables as well as variables for variouscharacteristics of the home itself (such as size, level of insulation,method of construction, etc.) and data from other houses andenvironments.

FIG. 31 illustrates the steps involved in diagnosing defects in the HVACsystem for specific home X. In step 3102, the server retrieves climatedata related to home X. Such data may include current outsidetemperature, outside temperature during the preceding hours, outsidehumidity, wind direction and speed, whether the sun is obscured byclouds, and other factors. In step 3104, the server retrieves HVAC dutycycle data for home X. Such data may include target settings set by thethermostat in current and previous periods, the timing of switch-on andswitch-off events and other data.

In step 3106, the server retrieves data regarding current and recenttemperature readings as recorded by the thermostat in home X. In step3108, the server retrieves profile data for home X. Such data mayinclude square footage and number of floors, when the house was builtand/or renovated, the extent to which it is insulated, its address,make, model and age of its furnace and air conditioning hardware, andother data. In step 3110, the server retrieves comparative data fromother houses that have thermostats that also report to the server.

Such data may include interior temperature readings, outside temperaturefor those specific locations, duty cycle data for the HVAC systems atthose locations, profile data for the structures and HVAC systems inthose houses and the calculated thermal mass index for those otherhouses. In step 3112, the server calculates the current relativeefficiency of home X as compared to other homes. Those comparisons willtake into account differences in size, location, age, etc in makingthose comparisons.

The server will also take into account that comparative efficiency isnot absolute, but will vary depending on conditions. For example, ahouse that has extensive south-facing windows is likely to experiencesignificant solar gain. On sunny winter days, that home will appear moreefficient than on cloudy winter days. That same house will appear moreefficient at times of day and year when trees or overhangs shade thosewindows than it will when summer sun reaches those windows. Thus theserver will calculate efficiency under varying conditions.

In step 3114 the server compares the HVAC system's efficiency, correctedfor the relevant conditions, to its efficiency in the past. If thecurrent efficiency is substantially the same as the historicalefficiency, the server concludes 3116 that there is no defect and thediagnostic routine ends. If the efficiency has changed, the serverproceeds to compare the historical and current data against patterns ofchanges known to indicate specific problems. For example, in step 3118,the server compares that pattern of efficiency changes against the knownpattern for a clogged air filter, which is likely to show a slow,gradual degradation over a period of weeks or even months.

If the pattern of degradation matches the clogged filter paradigm, theserver creates and transmits to the homeowner a message 3120 alertingthe homeowner to the possible problem. If the problem does not match theclogged filter paradigm, the system compares 3122 the pattern to theknown pattern for a refrigerant leak, which is likely to show adegradation over a period of a few hours to a few days. If the patternof degradation matches the refrigerant leak paradigm, the server createsand transmits to the homeowner a message 3124 alerting the homeowner tothe possible problem.

If the problem does not match the refrigerant leak paradigm, the systemcompares 3126 the pattern to the known pattern for an open window ordoor, which is likely to show significant changes for relatively shortperiods at intervals uncorrelated with climatic patterns. If the patternof degradation matches the open door/window paradigm, the server createsand transmits to the homeowner a message 3128 alerting the homeowner tothe possible problem. If the problem does not match the refrigerant leakparadigm, the system continues to step through remaining know patterns N3130 until either a pattern is matched 3132 or the list has beenexhausted without a match 3134.

FIG. 32 illustrates the steps involved in diagnosing inaccuratethermostat readings due to improper location. In step 3202, the serverretrieves climate data related to home X. Such data may include currentoutside temperature, outside temperature during the preceding hours,outside humidity, wind direction and speed, whether the sun is obscuredby clouds, and other factors. In step 3204, the server retrieves HVACduty cycle data for home X. Such data may include target settings set bythe thermostat in current and previous periods, the timing of switch-onand switch-off events and other data.

In step 3206, the server retrieves data regarding current and recenttemperature readings as recorded by the thermostat in home X. In step3208, the server retrieves profile data for home X. Such data mayinclude square footage and number of floors, when the house was builtand/or renovated, the extent to which it is insulated, its address,make, model and age of its furnace and air conditioning hardware, andother data.

In step 3210, the server retrieves comparative data from other housesthat have thermostats that also report to the server. Such data mayinclude interior temperature readings, outside temperature for thosespecific locations, duty cycle data for the HVAC systems at thoselocations, profile data for the structures and HVAC systems in thosehouses and the calculated thermal mass index for those other houses.

In step 3212, the server calculates the expected thermostat temperaturereading based upon the input data. In step 3214, the server compares thepredicted and actual values. If the calculated and actual values are atleast roughly equivalent, the server concludes 3216 that there is nothermostat-related anomaly. If the calculated and actual values are notroughly equivalent, the server retrieves additional historicalinformation about past thermostat readings in step 3218.

In step 3220, the server retrieves solar progression data, i.e.,information regarding the times at which the sun rises and sets on thedays being evaluated at the location of the house being evaluated, andthe angle of the sun at that latitude, etc. In step 3222, the servercompares the characteristics of the anomalies over time, to see if, forexample, abnormally high readings began at 3:06 on June 5th, 3:09 onJune 6th, 3:12 on June 7th, and the solar progression data suggests thatat the house being analyzed, that sun would be likely to reach a givenplace in that house three minutes later on each of those days.

If the thermostat readings do not correlate with the solar progressiondata, the server concludes 3224 that the sun is not causing thedistortion by directly hitting the thermostat. If the thermostatreadings do correlate with solar progression, the server then calculates3226 the predicted duration of the distortion caused by the sun.

In step 3228, the server calculates the appropriate setpoint informationto be used by the thermostat to maintain the desired temperature andcorrect for the distortion for the expected length of the event. Forexample, if the uncorrected setpoint during the predicted event is 72degrees, and the sun is expected to elevate the temperature reading byeight degrees, the server will instruct the thermostat to maintain asetpoint of 80 degrees. In step 3230, the server sends the homeowner amessage describing the problem.

One or more embodiments of the invention may be used to implementadditional energy savings by implementing small, repeated changes insetpoint. Because energy consumption is directly proportional tosetpoint—that is, the further a given setpoint diverges from the balancepoint (the natural inside temperature assuming no HVAC activity) in agiven house under given conditions, the higher energy consumption willbe to maintain temperature at that setpoint), energy will be saved byany strategy that over a given time frame lowers the average heatingsetpoint or raises the cooling setpoint.

It is therefore possible to save energy by adopting a strategy thattakes advantage of human insensitivity to slow temperature ramping byincorporating a user's desired setpoint within the range of the ramp,but setting the average target temperature below the desired setpoint inthe case of heating, and above it in the case of cooling. For example, aramped summer setpoint that consisted of a repeated pattern of threephases of equal length set at 72° F., 73° F., and 74° F. would create aneffective average setpoint of 73° F., but would generally be experiencedby occupants as yielding at least roughly equivalent comfort as in aroom set at a constant 72° F. Energy savings resulting from thisapproach have been shown to be in the range of 4-6%.

Embodiments of the invention can automatically generate optimized rampedsetpoints that could save energy without compromising the comfort of theoccupants. It would also be advantageous to create a temperature controlsystem that could incorporate adaptive algorithms that couldautomatically determine when the ramped setpoints should not be applieddue to a variety of exogenous conditions that make application of suchramped setpoints undesirable.

FIG. 33 represents the conventional programming of a thermostat and theresulting behavior of a home's HVAC system in the air conditioningcontext. The morning setpoint 3302 of 74 degrees remains constant frommidnight until 9:00 AM, and the inside temperature 3304 varies more orless within the limits of the hysteresis band during that entire period.When the setpoint changes to 80 degrees 3306, the inside temperature3308 varies within the hysteresis band around the new setpoint, and soon. Whether the average temperature is equal to, greater or less thanthe nominal setpoint will depend on weather conditions, the dynamicsignature of the structure, and the efficiency and size of the HVACsystem. But in most cases the average temperature will be at leastroughly equivalent to the nominal setpoint.

FIG. 34 represents implementation of a three-phase ramped setpointderived from the same user preferences as manifested by the settingsshown in FIG. 33. Thus the user-selected setpoint for the morning isstill 74 degrees, and is reflected in the setpoint 3404 at the start ofeach three-step cycle, but because (in the air conditioning context) thesetpoint requested by the user is the lowest of the three discretesteps, rather than the middle step, the average setpoint will be onedegree higher 3402, and the resulting average inside temperature will beroughly one degree warmer than the average temperature without use ofthe ramped setpoints, thereby saving energy.

In the currently preferred embodiment, the implementation of the rampedsetpoints may be dynamic based upon both conditions inside the structureand other planned setpoint changes. Thus, for example, the rampedsetpoints 3406, 3408 and 3410 may be timed so that the 9 AM change inuser-determined setpoint from 74 degrees to 80 degrees is in effectanticipated, and the period in which the air conditioner is not used canbe extended prior to the scheduled start time for the lessenergy-intensive setpoint. Similarly, because the server 106 is awarethat a lower setpoint will begin at 5 PM. The timing can be adjusted toavoid excessively warm temperatures immediately prior to the scheduledsetpoint change, which could cause noticeable discomfort relative to thenew setpoint if the air conditioner is incapable of quickly reducinginside temperature on a given day based upon the expected slope ofinside temperatures at that time 3412.

In order to implement such ramped setpoints automatically, algorithmsmay be created. These algorithms may be generated and/or executed asinstructions on remote server 106 and the resulting setpoint changes canbe transmitted to a given thermostat on a just-in-time basis or, if thethermostat 108 is capable of storing future settings, they may betransferred in batch mode to such thermostats. Basic parameters used togenerate such algorithms include:

the number of discrete phases to be used;

the temperature differential associated with each phase; and

the duration of each phase

In order to increase user comfort and thus maximize consumer acceptance,additional parameters may be considered, including:

time of day

outside weather conditions

recent history of manual inputs

recent pre-programmed setpoint changes.

Time of day may be relevant because, for example, if the home istypically unoccupied at a given time, there is no need for perceptualprogramming. Outside weather is relevant because comfort is dependentnot just on temperature as sensed by a thermostat, but also includesradiant differentials. On extremely cold days, even if the insidedry-bulb temperature is within normal comfort range, radiant losses dueto cold surfaces such as single-glazed windows can cause subjectivediscomfort; thus on such days occupants may be more sensitive toramping. Recent manual inputs (e.g., programming overrides) may createsituations in which exceptions should be taken; depending on thecontext, recent manual inputs may either suspend the ramping ofsetpoints or simply alter the baseline temperature from which theramping takes place.

FIG. 35 shows the steps used in the core ramped setpoint algorithm inthe context of a remotely managed thermostat system. In step 3502 theapplication determines whether to instantiate the algorithm based uponexternal scheduling criteria. In step 3504 the application running on aremote server retrieves from the thermostat the data generated by orentered into the thermostat, including current temperature settings,HVAC status and inside temperature. The algorithm performs preliminarylogical tests at that point to determine whether further processing isrequired. For example, in the heating context, if the inside temperatureas reported by the thermostat 108 is more than 1 degree higher than thecurrent setpoint, the algorithm may determine that running the rampedsetpoint program will have no effect and therefore terminate.

In step 3506 the algorithm advances to the next phase from the mostrecent phase; i.e., if the algorithm is just starting, the phase changesfrom “0” to “1”; if it has just completed the third phase of athree-phase ramp, the phase will change from “2” to “0”. In step 3508the application determines if the current phase is “0”. If it is, thenin step 3510 the algorithm determines whether current setpoint equalsthe setpoint in the previous phase. If so, which implies no manualoverrides or other setpoint adjustments have occurred during the mostrecent phase, then in step 3512 the algorithm sets the new setpoint backto the previous phase “0” setpoint. If not, then in step 3514, thealgorithm keeps the current temperature setting as setpoint for this newphase. In step 3516, the algorithm logs the resulting new setpoint asthe new phase “0” setpoint for use in subsequent phases.

Returning to the branch after step 3508, if the current phase at thatpoint is not phase “0”, then in step 3520, the algorithm determineswhether the current setpoint is equal to the setpoint temperature in theprevious phase. If not, which implies setpoints have been adjusted bythe house occupants, thermostat schedules, or other events, then in step3522, the application resets the phase to “0”, resets the new setpointassociated with phase “0” to equal the current temperature setting, andsets the current setting to that temperature. Alternatively, if thecurrent temperature setting as determined in step 3520 is equal to thesetpoint in the previous phase, then in step 3524 new setpoint is madeto equal current setpoint plus the differential associated with eachphase change. In step 3526 the “previous-phase setpoint” variable isreset to equal the new setpoint in anticipation of its use during asubsequent iteration.

FIG. 36 shows one embodiment of the overall control applicationimplementing the algorithm described in FIG. 35. In step 3602, thecontrol application retrieves the current setting from the thermostat.In step 3604, the setting is logged in database 300. In step 3606, thecontrol program determines whether other algorithms that have higherprecedence than the ramped setpoint algorithm are to be run. If anotheralgorithm is to be run prior to the ramped setpoint algorithm, then theother program is executed in step 3608. If there are no alternatealgorithms that should precede the ramped setpoint application then instep 1310, the control program determines whether the thermostat hasbeen assigned to execute the ramped setpoint program. If not, thecontrol program skips the remaining actions in the current iteration. Ifthe program is set to run, then in step 3612 the algorithm retrievesfrom database 300 the rules and parameters governing the implementationof the algorithm for the current application of the program.

In step 3614, the algorithm determines whether one or more conditionsthat preclude application of the algorithm, such as extreme outsideweather conditions, whether the home is likely to be occupied, etc. Ifany of the exclusionary conditions apply, the application skipsexecution of the ramped setpoint algorithm for the current iteration. Ifnot, the application proceeds to step 3616 in which the applicationdetermines whether the setpoint has been altered by manual overrides,thermostat setback schedule changes, or other algorithms as compared tothe previous value as stored in database 300. If setpoint has beenaltered, the application proceeds to step 3620 discussed below.

In step 3618, the program described in FIG. 35 is executed. In step3620, the application resets the phase to “0”. Certain temperaturesetting variables are reset in anticipation of their use in subsequentphases. These variables include the new phase 0 temperature settingwhich is anchored to the current actual temperature setting, and the newprevious-phase setpoint which will be used for identifying setpointoverrides in the subsequent phase.

In step 3622, the system records the changes to the thermostat settingsto database 300. In step 3624, the system records the changes to thephase status of the algorithm to database 300. In step 3626, theapplication determines whether the new temperature setting differs fromthe current setting. If they are the same, the application skipsapplying changes to the thermostat. If they are different, then in step3628, the application transmits revised settings to the thermostat. Instep 3630, the application then hibernates for the specified durationuntil it is invoked again by beginning at step 3602 again.

An embodiment of the subject invention may also be used to detectoccupancy through the use of software related to electronic deviceslocated inside the conditioned structure, such as the browser running oncomputer or other device 104. FIG. 37 represents the screen of acomputer or other device 104 using a graphical user interface connectedto the Internet. The screen shows that a browser 3700 is displayed oncomputer 104. In one embodiment, a background application installed oncomputer 104 detects activity by a user of the computer, such as cursormovement, keystrokes or otherwise, and signals the application runningon server 106 that activity has been detected. Server 106 may then,depending on context, (a) transmit a signal to thermostat 108 changingsetpoint because occupancy has been detected at a time when the systemdid not expect occupancy; (b) signal the background application runningon computer 104 to trigger a software routine that instantiates a pop-upwindow 3702 that asks the user if the server should change the currentsetpoint, alter the overall programming of the system based upon a newoccupancy pattern, etc. The user can respond by clicking the cursor on“yes” button 3704 or “No” button 3706. Equivalent means of signallingactivity may be employed with interactive television programming, gamingsystems, etc.

FIG. 38 is a flowchart showing the steps involved in the operation ofone embodiment of the subject invention. In step 3802, computer 104transmits a message to server 106 via the Internet indicating that thereis user activity on computer 104. This activity can be in the form ofkeystrokes, cursor movement, input via a television remote control, etc.In step 3804 the application queries database 300 to retrieve settinginformation for the HVAC system. In step 3806 the application determineswhether the current HVAC program is intended to apply when the home isoccupied or unoccupied.

If the HVAC settings then in effect are intended to apply for anoccupied home, then the application terminates for a specified interval.If the HVAC settings then in effect are intended to apply when the homeis unoccupied, then in step 3808 the application will retrieve fromdatabase 300 the user's specific preferences for how to handle thissituation. If the user has previously specified (at the time that theprogram was initially set up or subsequently modified) that the userprefers that the system automatically change settings under suchcircumstances, the application then proceeds to step 3816, in which itchanges the programmed setpoint for the thermostat to the settingintended for the house when occupied. If the user has previouslyspecified that the application should not make such changes withoutfurther user input, then in step 3810 the application transmits acommand to computer 104 directing the browser to display a messageinforming the user that the current setting assumes an unoccupied houseand asking the user in step 3812 to choose whether to either keep thecurrent settings or revert to the pre-selected setting for an occupiedhome. If the user selects to retain the current setting, then in step3814 the application will write to database 300 the fact that the usershas so elected and terminate. If the user elects to change the setting,then in step 3816 the application transmits the revised setpoint to thethermostat. In step 3814 the application writes the updated settinginformation to database 300.

FIG. 39 is a flowchart that shows how an embodiment of the invention canbe used to select different HVAC settings based upon its ability toidentify which of multiple potential occupants is using the computerattached to the system. In step 3902 computer 104 transmits to server106 information regarding the type of activity detected on computer 104.Such information could include the specific program or channel beingwatched if, for example, computer 104 is used to watch television. Theinformation matching, for example, TV channel 7 at 4:00 PM on a givendate to specific content may be made by referring to Internet-based orother widely available scheduling sources for such content. In step 3904server 106 retrieves from database 300 previously logged data regardingviewed programs. In step 3906 server 106 retrieves previously storeddata regarding the residents of the house.

For example, upon initiating the service, one or more users may havefilled out online questionnaires sharing their age, gender, schedules,viewing preferences, etc. In step 3908, server 106 compares the receivedinformation about user activity to previously stored informationretrieved from database 300 about the occupants and their viewingpreferences. For example, if computer 104 indicates to server 106 thatthe computer is being used to watch golf, the server may conclude thatan adult male is watching; if computer 104 indicates that it is beingused to watch children's programming, server 106 may conclude that achild is watching.

In step 3910 the server transmits a query to the user in order to verifythe match, asking, in effect, “Is that you Bob?” In step 3912, basedupon the user's response, the application determines whether the correctuser has been identified. If the answer is no, then the applicationproceeds to step 3916. If the answer is yes, then in step 3914 theapplication retrieves the temperature settings for the identifiedoccupant. In step 3916 the application writes to database 300 theprogramming information and information regarding matching of users tothat programming.

In an alternative embodiment, the application running on computer 104may respond to general user inputs (that is, inputs not specificallyintended to instantiate communication with the remote server) byquerying the user whether a given action should be taken. For example,in a system in which the computer 104 is a web-enabled television orweb-enabled set-top device connected to a television as a display,software running on computer 104 detects user activity, and transmits amessage indicating such activity to server 106. The trigger for thissignal may be general, such as changing channels or adjusting volumewith the remote control or a power-on event. Upon receipt by server 106of this trigger, server 106 transmits instructions to computer 104causing it to display a dialog box asking the user whether the userwishes to change HVAC settings.

Additional functionality is also envisioned as part of differentembodiments of the invention. For example, information from historicdata may be used to predict how long it will take a user to reach homefrom the current coordinates, and the estimated arrival time may be usedto calculate optimal cycling strategies for the HVAC system. Inaddition, information about traffic conditions may be integrated intothese calculations, so that the geospatial data relative to mobiledevice 105 may indicate that a user is taking his or her normal route,but because of a traffic jam, is likely to come home later than wouldotherwise be expected. The characteristics of a given location may beused to infer arrival times as well. For example, if the geospatial dataindicates that the user of mobile device 105 has arrived at thesupermarket on his way home, a delay of 20 minutes is likely, whereas ifthe user has parked at a restaurant, the delay is likely to be one hour.

It is also possible to incorporate more sophisticated heuristics inincorporating the varying preferences of multiple occupants of a givenhome or other structure. For example, rules can be structured so thatUser #1's preferences control during the heating season, but not duringthe cooling season; User #2's preferences might control during certaintimes of the day but not others; User #3's preferences may takeprecedence whenever they result in a more energy efficient strategy, butnot when they result in increased energy use, and so on.

While particular embodiments of the present invention have been shownand described, it is apparent that changes and modifications may be madewithout departing from the invention in its broader aspects, and,therefore, that the invention may be carried out in other ways withoutdeparting from the true spirit and scope. These and other equivalentsare intended to be covered by the following claims:

What is claimed is:
 1. A thermostat system comprising: a housing;electrical contacts configured to connect the thermostat with wires thatallow for at least two electrical connections from a building's HVACsystem to the contacts; a display configured to present information to auser; a wireless radio compatible with a wireless radio frequencyprotocol and configured to communicate bi-directionally with alocation-aware mobile-device; a temperature sensor; one or moreprocessors configured with electronic circuitry to: receive HVAC dataparameters, including a first data parameter from the temperature sensorcomprising an interior temperature inside the building; and a seconddata parameter from a humidity sensor comprising an interior humidityinside building; determine a first temperature setpoint data parameterfor the building, wherein the first setpoint data parameter includes afirst temperature value and a first time value; determine a secondtemperature setpoint data parameter for the building, wherein the secondsetpoint data parameter includes a second temperature value and a secondtime value; receive radio frequency signals from the location-awaremobile device; receive geo-positioning data from the location-awaremobile device and automatically adjust a temperature value based on thegeo-positioning data, including initiating at least one cooling orheating cycle for the HVAC system when the geo-positioning data isdetermined to indicate that the building is unoccupied by the user;electronic circuitry configured to allow the user to adjust a desiredtemperature for the HVAC system; electronic circuitry configured toanalyze a plurality of data parameters specific to the user, includingat least one data parameter relating to usage of the HVAC system atvarious times; and electronic circuitry configured to generate andcommunicate usage metrics pertaining to the HVAC system over time; and acompressor delay circuit configured to delay the start or stop of acompressor for the HVAC system and protect the compressor from rapidcycling.
 2. The thermostat system of claim 1, wherein the one or moreprocessors is further configured to receive a third data parameter froma network connected to the thermostat, wherein the third data parametercomprises an outside temperature from a source external to the building.3. The thermostat system of claim 1, wherein the one or more processorsis further configured to receive a third data parameter from one or moremotion sensors located within the interior of the building.
 4. Thethermostat system of claim 1, wherein the one or more processors isfurther configured to determine whether the building is occupied orunoccupied.
 5. The thermostat system of claim 1, wherein the one or moreprocessors is further configured to receive a third data parameter froma first sensor, wherein the third data parameter from the first sensorincludes a measurement of at least one characteristic of a buildingwithin which the thermostat is located.
 6. The thermostat system ofclaim 2, wherein the third data parameter from the network connected tothe thermostat further comprises a measurement of the outdoor humidity.7. The thermostat system of claim 4, wherein the determination ofwhether the building is occupied or unoccupied by the one or moreprocessors is based at least in part on a fourth data parameter receivedfrom a motion sensor.
 8. The thermostat system of claim 1, wherein theone or more processors is further configured to predict, based at leaston the first data parameter, the second data parameter, and the firsttemperature setpoint data parameter, the time necessary for the HVACsystem to operate in order for the building to reach the firsttemperature value by the first time value.
 9. The thermostat system ofclaim 11, wherein the one or more processors' prediction of the timenecessary for the HVAC system to operate in order to reach the firsttemperature value by the first time value is further based ondetermining a rate of change value necessary for the building to reachthe first temperature value by the first time value.
 10. The thermostatsystem of claim 12, wherein the automatic adjustment of the temperaturesetpoint is based at least in part on the prediction of time necessaryfor the HVAC system to operate in order to reach the first temperaturevalue by the first time value.
 11. The thermostat system of claim 13,wherein the automatic adjustment of the temperature setpoint is based atleast in part on the rate of change value.
 12. The thermostat system ofclaim 12, further comprising a memory configured to store historicalvalues of the first data parameter and second data parameter; whereinthe one or more processors' prediction of the time necessary for theHVAC system to operate in order to reach the first temperature value bythe first time value is further based on historical values of the firstdata parameter and second data parameter.
 13. The thermostat system ofclaim 15 wherein the one or more processors determines one or moreintermediate temperature setpoints that have a temperature value betweena current temperature of the building and the first temperaturesetpoint.
 14. The thermostat system of claim 16, wherein the automaticadjustment of the temperature setpoint is based at least in part on theprediction of time necessary for the HVAC system to operate in order toreach the first temperature value by the first time value.
 15. Thethermostat system of claim 1, wherein the one or more processors isfurther configured to receive a third data parameter generated based atleast in part on a previous operation of the HVAC system, wherein thethird data parameter includes at least one performance characteristic ofthe HVAC system.
 16. A thermostat system comprising: a housing;electrical contacts configured to connect the thermostat with wires thatallow for at least two electrical connections from a building's HVACsystem to the contacts; a display configured to present information to auser; a wireless radio compatible with a wireless radio frequencyprotocol and configured to communicate bi-directionally with alocation-aware mobile-device; a temperature sensor; one or moreprocessors configured with electronic circuitry to: receive HVAC dataparameters, including a first data parameter from the temperature sensorcomprising an interior temperature inside the building; and a seconddata parameter from one or more motions sensors located within theinterior of the building; determine a first temperature setpoint dataparameter for the building, wherein the first setpoint data parameterincludes a first temperature value and a first time value; determine asecond temperature setpoint data parameter for the building, wherein thesecond setpoint data parameter includes a second temperature value and asecond time value; receive radio frequency signals from thelocation-aware mobile device; receive geo-positioning data from thelocation-aware mobile device and automatically adjust a temperaturevalue based on the geo-positioning data, including initiating at leastone cooling or heating cycle for the HVAC system when thegeo-positioning data is determined to indicate that the building isunoccupied by the user; electronic circuitry configured to allow theuser to adjust a desired temperature for the HVAC system; electroniccircuitry configured to analyze a plurality of data parameters specificto the user, including at least one data parameter relating to usage ofthe HVAC system at various times; and electronic circuitry configured togenerate and communicate usage metrics pertaining to the HVAC systemover time; and a compressor delay circuit configured to delay the startor stop of a compressor for the HVAC system and protect the compressorfrom rapid cycling.
 17. The thermostat system of claim 19, wherein theone or more processors is further configured to receive a third dataparameter from a network connected to the thermostat, wherein the thirddata parameter comprises an outside temperature collected from a sourceexternal to the building.
 18. The thermostat system of claim 19, whereinthe one or more processors is further configured to receive a third dataparameter from a humidity sensor detecting a humidity of the interior ofthe building.
 19. The thermostat system of claim 19, wherein the one ormore processors is further configured to determine whether the buildingis occupied or unoccupied.
 20. The thermostat system of claim 19,wherein the one or more processors is further configured to receive athird data parameter from a first sensor, wherein the third dataparameter from the first sensor includes a measurement of at least onecharacteristic of a building within which the thermostat is located. 21.The thermostat system of claim 21, wherein the third data parameter fromthe network connected to the thermostat further comprises a measurementof the outdoor humidity.
 22. The thermostat system of claim 22, whereinthe determination of whether the building is occupied or unoccupied bythe one or more processors is based at least in part on the second dataparameter received from a motion sensor.
 23. The thermostat system ofclaim 19, wherein the one or more processors is further configured topredict, based at least on the first data parameter, the second dataparameter, and the first temperature setpoint data parameter, the timenecessary for the HVAC system to operate in order for the building toreach the first temperature value by the first time value.
 24. Thethermostat system of claim 29, wherein the one or more processors'prediction of the time necessary for the HVAC system to operate in orderto reach the first temperature value by the first time value is furtherbased on determining a rate of change value necessary for the buildingto reach the first temperature value by the first time value.
 25. Thethermostat system of claim 30, wherein the automatic adjustment of thetemperature setpoint is based at least in part on the prediction of timenecessary for the HVAC system to operate in order to reach the firsttemperature value by the first time value.
 26. The thermostat system ofclaim 31, wherein the automatic adjustment of the temperature setpointis based at least in part on the rate of change value.
 27. Thethermostat system of claim 30, further comprising a memory configured tostore historical values of the first data parameter and second dataparameter; wherein the one or more processors' prediction of the timenecessary for the HVAC system to operate in order to reach the firsttemperature value by the first time value is further based on historicalvalues of the first data parameter and second data parameter.
 28. Thethermostat system of claim 33 wherein the one or more processorsdetermines one or more intermediate temperature setpoints that have atemperature value between a current temperature of the building and thefirst temperature setpoint.
 29. The thermostat system of claim 34,wherein the automatic adjustment of the temperature setpoint is based atleast in part on the prediction of time necessary for the HVAC system tooperate in order to reach the first temperature value by the first timevalue.
 30. The thermostat system of claim 35, wherein the one or moreprocessors is further configured to receive a third data parametergenerated based at least in part on a previous operation of the HVACsystem, wherein the third data parameter includes at least oneperformance characteristic of the HVAC system.